"""simple docstring"""



import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__:str									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
    """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}






class  snake_case__      (       snake_case_       ):
 _snake_case       :					Tuple							  =						"""encodec"""


 def __init__( self    ,		lowerCamelCase=[1.5, 3.0, 6.0, 12.0, 24.0]    ,		lowerCamelCase=24000    ,		lowerCamelCase=1    ,		lowerCamelCase=False    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase=128    ,		lowerCamelCase=32    ,		lowerCamelCase=1    ,		lowerCamelCase=[8, 5, 4, 2]    ,		lowerCamelCase="weight_norm"    ,		lowerCamelCase=7    ,		lowerCamelCase=7    ,		lowerCamelCase=3    ,		lowerCamelCase=2    ,		lowerCamelCase=True    ,		lowerCamelCase="reflect"    ,		lowerCamelCase=2    ,		lowerCamelCase=2    ,		lowerCamelCase=1.0    ,		lowerCamelCase=1024    ,		lowerCamelCase=None    ,		lowerCamelCase=True    ,		**lowerCamelCase    ,		):
        __a       =    target_bandwidths
        __a       =    sampling_rate
        __a       =    audio_channels
        __a       =    normalize
        __a       =    chunk_length_s
        __a       =    overlap
        __a       =    hidden_size
        __a       =    num_filters
        __a       =    num_residual_layers
        __a       =    upsampling_ratios
        __a       =    norm_type
        __a       =    kernel_size
        __a       =    last_kernel_size
        __a       =    residual_kernel_size
        __a       =    dilation_growth_rate
        __a       =    use_causal_conv
        __a       =    pad_mode
        __a       =    compress
        __a       =    num_lstm_layers
        __a       =    trim_right_ratio
        __a       =    codebook_size
        __a       =    codebook_dim if codebook_dim is not None else hidden_size
        __a       =    use_conv_shortcut

        if self.norm_type not in ["weight_norm", "time_group_norm"]:
               raise ValueError(
                   F"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"						)

        super().__init__(**lowerCamelCase						)


 @property
 def 						a__   ( self						):
        if self.chunk_length_s is None:
               return None
        else:
               return int(self.chunk_length_s * self.sampling_rate						)


 @property
 def 						a__   ( self						):
        if self.chunk_length_s is None or self.overlap is None:
               return None
        else:
               return max(1    ,		int((1.0 - self.overlap) * self.chunk_length						)						)


 @property
 def 						a__   ( self						):
        __a       =    np.prod(self.upsampling_ratios						)
        return math.ceil(self.sampling_rate / hop_length						)






 @property
 def 						a__   ( self						):
        return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)						)

"""simple docstring"""



from typing import Union

import fire
import torch
from tqdm import tqdm



def 	_lowerCamelCase(	a   ,    a = "cpu"   ,    a = None				):
       __a       =    torch.load(a   ,    map_location=a				)
       for k, v in tqdm(state_dict.items()				):
              if not isinstance(a   ,    torch.Tensor				):
                     raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"				)
              __a       =    v.half()
       if save_path is None:  # overwrite src_path
              __a       =    src_path
       torch.save(a   ,    a				)


if __name__ == "__main__":
   fire.Fire(convert)
